The Book of Why - Deepstash

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Correlation Is Not Causation

When we see things happening together, it's not always enough to understand why. To truly understand, we need to ask what causes what.

Sometimes, things can seem related when they actually aren't. Just because two things are happening at the same time, doesn't mean one causes the other—it could just be a coincidence! Remember to always ask "why" and look for the real cause.

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Why Humans Think Causally

Our brains are wired to constantly ask "What if?" and "Why?", which takes us beyond just raw data and into our imaginations, explanations, and actions.

Through our constant curiosity, we can understand the reasons behind things, and even attempt to change or influence them.

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The Ladder of Causation

Pearl developed a ladder with 3 steps:

Seeing - finding connections between events

Doing - taking action to affect outcomes

Imagining - exploring alternative scenarios

To be truly intelligent, one must climb this ladder. 🧠

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Statistics Alone Can’t Answer ‘Why’

Statistics reveal "what is," but that isn't always enough. Understanding "what could be" and "what would have happened" requires causal reasoning.

  • Statistics show us the current state of things.
  • But to fully understand a situation, we need to consider other possibilities.
  • That's where causal reasoning comes in.

Using causal reasoning, we can explore the potential outcomes and consequences of different scenarios, providing a more complete understanding of a situation.

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Causal Diagrams Matter

Visual models like Directed Acyclic Graphs (DAGs) can help us understand links between factors and minimize biases, such as confounding variables.

They show us how one event or exposure leads to another and happen before it.

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Interventions Change the Game

To understand the effects of taking action, it's important to distinguish between observation and intervention. This process, known as causal inference, is more than just analyzing data.

Key concepts:

  • Intervention
  • Causal inference
  • Data mining

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Counterfactuals Are the Heart of Understanding

Asking "What if I had done X instead of Y?" helps us learn, plan, and assign responsibility.

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You Can Learn Causality from Data—With a Model 🎓

Judea Pearl asserts that simply gathering data is not enough to grasp causality. Instead, we need to create causal models that incorporate assumptions and establish relationships between variables.

Through causal modeling, we can identify key insights and patterns to better understand cause and effect. This allows us to answer questions such as "When A happens, what can we expect for B?" and make informed decisions based on data-driven evidence.

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The Myth of Data-Driven Everything

Don't be fooled by data! 📊🚫 Just because something happened, doesn't mean it caused it. Use reasoning to understand beyond what is seen.

- Data can't predict with certainty what would have happened 🤔

- Keep asking why something happened, until you understand the underlying causes 🔍

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Why Matters More Than What

To fully comprehend the world, solve issues, or make choices, we need to concentrate on "why" things occur, not just on data. Understanding "why" gives us the insight to make better decisions.

Why should we focus on "why"?

  • Allows for a deeper understanding of an issue or problem
  • Provides insight for better decision-making
  • Gives context to the data and the "what" in a situation
  • Leads to more effective problem-solving

🤔🔍🚀

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IDEAS CURATED BY

maks1

Wealth Building. Business. Investing. Crypto and Technology. Entrepreneurship. Reading, Learning and Writing.

CURATOR'S NOTE

"The Book of Why" explores how humans think about cause and effect. Judea Pearl revolutionizes science with causal reasoning, explaining how we can go beyond data correlation to ask deeper “why” questions—and build machines that truly understand.

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